Eczema is a complex disease, with heterogeneous presentations, clinical courses and outcomes. Despite this, our approach to characterising eczema is surprisingly unidimensional. Failure to capture disease complexity when characterising patients is a major stumbling block, which limits understanding of eczema aetiology. It also hinders the practice of stratified medicine. At present, we are failing our patients, with inadequate strategies and therapeutic interventions. Novel therapeutic agents for eczema are on the horizon; multidimensional phenotyping would allow us to understand how to better use these and existing therapies to help our patients, by allowing us to tailor treatments for individual patients (personalized medicine). Understanding phenotypes would also help inform prevention strategies, which are important giving the rising prevalence of eczema.
We will use novel bioinformatic and statistical approaches to identify specific groups of individuals with eczema (phenotypes) in the Avon Longitudinal Study of Parents and Children (ALSPAC) prospective birth cohort study. Phenotypes will combine a variety of multidimensional characteristics including characteristics of the participant’s eczema and clinical history, immunological, genetic and novel biomarker characteristics. We will then assess predictors of clinical phenotypes.